May 18, 2007

Paul Rhodes

At Almaden, we had an amazing talk by Dr. Paul Rhodes of Evolved Machines, Inc. and Stanford University. He gave us a preview of some unpublished work.

Title:Neuronal components are connected with stochastic and dynamic synapses: implications for biological and synthetic neural computation

Abstract:The sensory and motor performance displayed by biological systems is an existence proof arguing for the value of reverse-engineering brain circuits. In biological neuronal circuits the synapses mediating transmission between neurons are stochastic devices, so that for example an incoming signal might be relayed with a probability of 30% at each of the 1,000’s of synapses formed by an axon. Further, transmission probability itself dynamically changes as a function of the recent pattern of input, so that a given synapse may increase or decrease its probability during a train of incoming signals, in a frequency-dependent manner. Finally, these dynamics are specific to the subtypes of neurons being connected. Among the consequences of this set of properties is that various elements of a complex cortical circuit may fade or emerge differentially as a function of the pattern of sensory activity. Clearly the simulation of biological neural circuits, on any scale, requires incorporation of synaptic connections which adequately capture the dynamic and connection-specific stochastic properties of transmission. Here I present a comprehensive model of cortical synapses, incorporating 5 interacting biophysically interpretable mechanisms which together enable the computation of transmission probability in a manner closely matched to experimental data. These model synapses may be used both in biologically plausible models of cortical circuitry as well as in synthetic neural arrays allowing exploration of the functional role and benefits of pattern-specific stochastic transmission in biological and artificial neural systems.

Biography:Dr. Rhodes is a Visiting Scholar at Stanford University and the head of Evolved Machines, Inc., a neural circuit applications research group in Palo Alto.

Dr. Rhodes academic research in the 1990’s concerned simulations of cortical neurons and circuits, including the first model of bursting in cortical pyramidal cells (Rhodes and Gray 1994), the prediction of backpropagation in dendritic trees, and studies of the functional implications of synaptic integration in active dendritic trees (summarized in a review in Cerebral Cortex, 1999). He is the founder of Evolved Machines, Inc., a research organization pioneering the synthesis of artificial neural circuits and their application to olfaction and visual object recognition. The group is developing the first synthetic neural arrays which wire themselves by simulating neural circuit growth in 3-dimensions, and is the first company to harness the power of programmable GPU’s for the simulation of neural computation, now achieving > 100-fold acceleration of the computing power of conventional cores. The company’s goal is the development of the first generation of devices truly based on brain circuitry, pioneering the fusion of neuroscience and engineering to develop new categories of machines which embed some of the capacities of biological neural systems.

Dr. Rhodes received a Ph.D. and M.S. in Neuroscience under Dr. Rodolfo Llinás at NYU Medical School, and received an M.S. in Physics from Stanford University after graduating with an A.B. in Physics, Magna cum Laude, from Harvard University.